Problem Statement
Traditional sentiment analysis models often focus on analyzing text alone,
leaving out valuable information from images and audio data. There is a need for
a more comprehensive sentiment analysis system that can capture user
sentiment across multiple modalities
Abstract
The Multi-Modal Sentiment Analysis project aims to develop a sentiment analysis
model that integrates text, images, and audio data. By combining information
from different media, the model will provide a more holistic understanding of
user sentiment, allowing for more accurate and nuanced sentiment analysis.
Outcome
● Improved accuracy in sentiment analysis by considering multiple
modalities.
● Enhanced understanding of user sentiment across diverse media types.
● Increased applicability in real-world scenarios, such as social media
monitoring and customer feedback analysis.
Reference
In this information age, opinion mining which is also known as sentiment analysis turns up to be the most important task in the field of natural language processing. Previous literature in area of sentiment analysis which mostly focused on single modality that is on textual data. Almost all the latest advancement in the sentiment analysis are using textual dataset and resources only. With the invent of internet which increases the use of social media, people are using vlogs, videos, pictures, audios, emojis and microblogs to represent their opinions on different web platforms. In this new media age, every day 720k hours of videos are uploaded on alone Youtube only. We have number of such platforms like YouTube. In the classical methods other modalities’ expressiveness is overlooked and thus these methods fail to generate accurate results. Numerous commercial applications used the aggregation of sentiments and opinions of individuals by anticipating large population.
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